The effects of mediation on the relationships between explanatory and outcome variables were evaluated simultaneously through estimating combined associations between experience (EXP) and mediator, and between mediator and weight management approach (WMA). Mediation was assessed through evaluating the indirect effect (pathway c`) [39] and comparing the strength and direction of this effect relative to the average direct effect (pathway c). Dark lines with arrow heads indicate significant relationships, whilst grey lines with blunt ends indicate non-significant relationships. Figure created with Biorender.com.</p
In the initial mediations (a and b) there was no covariate. Follow-up mediations (a and b) included ...
Mediation analysis is built to answer not only if one variable affects another, but how the effect t...
Mediational analysis is used to explain how a predictor affects the outcome through an intervening v...
<p>The figure displays the general mediation model that has been used to estimate effects according ...
<p>X: predictor variable; Y: outcome variable; M: mediator variable; a path: association between pre...
<p>Notes: a- indirect effects b- direct effects c- total effect. Numbers refer to coefficient (bias-...
<p>Panel A illustrates the total effect of the independent variable (IV) on the dependent variable (...
Path a denotes the association between depression and sense of control; path b indicates the path be...
Structurally relationship of variables is important in deeply analysis of path models, but the proce...
<p>* <i>p</i> < .05. <i>Note</i>. CRQ = Co-Rumination Questionnaire; TDI = Teate Depression Inventor...
<p>Note: Direct effect (R2 = .01) and indirect effect through the mediator (R2 = .04). Coefficient p...
<p>The paths represent the links between the variables of interest, weighted by the provided standar...
A (exposure) is age at diagnosis ≥ 55, M (mediator) is TERT promoter mutation, Y (outcome) is overal...
Standardized path coefficients are reported. The path coefficient in parentheses represents the tota...
<p>Left: voxel based analysis find a cluster of Precuneus showed significant mediation effect after ...
In the initial mediations (a and b) there was no covariate. Follow-up mediations (a and b) included ...
Mediation analysis is built to answer not only if one variable affects another, but how the effect t...
Mediational analysis is used to explain how a predictor affects the outcome through an intervening v...
<p>The figure displays the general mediation model that has been used to estimate effects according ...
<p>X: predictor variable; Y: outcome variable; M: mediator variable; a path: association between pre...
<p>Notes: a- indirect effects b- direct effects c- total effect. Numbers refer to coefficient (bias-...
<p>Panel A illustrates the total effect of the independent variable (IV) on the dependent variable (...
Path a denotes the association between depression and sense of control; path b indicates the path be...
Structurally relationship of variables is important in deeply analysis of path models, but the proce...
<p>* <i>p</i> < .05. <i>Note</i>. CRQ = Co-Rumination Questionnaire; TDI = Teate Depression Inventor...
<p>Note: Direct effect (R2 = .01) and indirect effect through the mediator (R2 = .04). Coefficient p...
<p>The paths represent the links between the variables of interest, weighted by the provided standar...
A (exposure) is age at diagnosis ≥ 55, M (mediator) is TERT promoter mutation, Y (outcome) is overal...
Standardized path coefficients are reported. The path coefficient in parentheses represents the tota...
<p>Left: voxel based analysis find a cluster of Precuneus showed significant mediation effect after ...
In the initial mediations (a and b) there was no covariate. Follow-up mediations (a and b) included ...
Mediation analysis is built to answer not only if one variable affects another, but how the effect t...
Mediational analysis is used to explain how a predictor affects the outcome through an intervening v...